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<table width="100%" summary="page for education"><tr><td>education</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Education Expenditure Data</h2>

<h3>Description</h3>

<p>Education Expenditure Data, from Chatterjee and Price (1977,
p.108).  This data set, representing the education expenditure
variables in the 50 US states, providing an interesting example of
heteroscedacity.
</p>


<h3>Usage</h3>

<pre>data(education, package="robustbase")</pre>


<h3>Format</h3>

<p>A data frame with 50 observations on the following 6 variables.
</p>

<dl>
<dt><code>State</code></dt><dd><p>State</p>
</dd>
<dt><code>Region</code></dt><dd><p>Region (1=Northeastern, 2=North central, 3=Southern, 4=Western)</p>
</dd>
<dt><code>X1</code></dt><dd><p>Number of residents per thousand residing in urban areas in 1970</p>
</dd>
<dt><code>X2</code></dt><dd><p>Per capita personal income in 1973</p>
</dd>
<dt><code>X3</code></dt><dd><p>Number of residents per thousand under 18 years of age in 1974</p>
</dd>
<dt><code>Y</code></dt><dd><p>Per capita expenditure on public education in a
state, projected for 1975</p>
</dd>
</dl>



<h3>Source</h3>

<p>P. J. Rousseeuw and A. M. Leroy (1987)
<em>Robust Regression and Outlier Detection</em>;
Wiley, p.110, table 16.
</p>


<h3>Examples</h3>

<pre>
data(education)
education.x &lt;- data.matrix(education[, 3:5])


summary(lm.education &lt;- lm(Y ~ Region + X1+X2+X3, data=education))


## See  example(lmrob.M.S) # for how robust regression is used
</pre>


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